3 Ways to Merge Pandas DataFrames

Three main ways to merge Pandas DataFrames:

1. merge()

The merge() method is the most common way to merge DataFrames. It takes two DataFrames as input and merges them on one or more common columns. The merge() method returns a new DataFrame that contains the rows from both DataFrames that have matching values in the common columns.

For example, the following code merges two DataFrames called df1 and df2 on the Name column:

df_merged = pd.merge(df1, df2, on='Name')

This will create a new DataFrame called df_merged that contains all of the rows from df1 and df2 that have matching values in the Name column.

2. join()

The join() method is similar to the merge() method, but it only merges DataFrames on a single column. The join() method returns a new DataFrame that contains the rows from the left DataFrame that have matching values in the column specified by the on parameter.

For example, the following code joins the df1 DataFrame on the Name column with the df2 DataFrame:

df_joined = df1.join(df2, on='Name')

This will create a new DataFrame called df_joined that contains all of the rows from df1 that have matching values in the Name column.

3. concat()

The concat() method is used to concatenate DataFrames together. The concat() method takes a list of DataFrames as input and concatenates them along the specified axis. The default axis is 0, which means that the DataFrames are concatenated along the rows.

For example, the following code concatenates the df1 and df2 DataFrames along the rows:

df_concatenated = pd.concat([df1, df2], axis=0)

This will create a new DataFrame called df_concatenated that contains all of the rows from df1 and df2.

The three methods described above are the most common ways to merge Pandas DataFrames. The method that you choose to use will depend on your specific needs.